Mini Project #03: Visualizing and Maintaining the Green Canopy of NYC

Executive Summary

New York City’s rich network of parks and green spaces is a vital part of urban life, supported through continuous investment by the municipal government and more than 550 nonprofit and community organizations. In this project, I will retrieve and analyze several open datasets via API calls, including the NYC City Council District Boundaries, NYC Tree Census, tree-related safety risk reports, and ongoing maintenance order records. Using these data sources, I will conduct an exploratory analysis and develop insights that culminate in a government project proposal aimed at enhancing the environmental and tree health conditions within District 32.

Task # 1: Download NYC City Council District Boundaries

suppressPackageStartupMessages({
  library(sf)
  library(fs)})

NYC_City_Council <- function(url){
  mp03 <- file.path("data", "mp03")
  if (!dir.exists(mp03)) {
    dir.create(mp03, showWarnings=FALSE, recursive=TRUE)
  }
  
  zip_path <- file.path(mp03, "NYC City Council District Boundaries.zip")
  if (!file.exists(zip_path)) {
    download.file(url,
                  destfile = zip_path,
                  mode = "wb")
  }
  
  shp_file <- dir_ls(mp03, recurse = TRUE, glob = "*.shp")
  if (length(shp_file) == 0) {
    unzip(zip_path, exdir = mp03)
    shp_file <- dir_ls(mp03, recurse = TRUE, glob = "*.shp")
  }
  
  NYC_file <- st_read(shp_file[1], quiet = TRUE)
  NYC_file <- st_transform(NYC_file, crs = "WGS84")
  
  return(NYC_file)
}

council <- NYC_City_Council("https://s-media.nyc.gov/agencies/dcp/assets/files/zip/data-tools/bytes/city-council/nycc_25c.zip")

Task # 2: Download Tree Points

suppressPackageStartupMessages({
  library(httr2)
  library(dplyr)})

Tree_Points <- function(url) {
  mp03 <- file.path("data", "mp03")
  limit <- 1000
  offset <- 0
  page <- 1
  all_files <- c()
  temp <- TRUE
  
  while (temp) {
    name <- file.path(mp03, paste0("treepoints", page, ".geojson"))
    
    if (!file_exists(name)) {
      request(url) |>
        req_url_query(`$limit` = limit, `$offset` = offset) |>
        req_perform() |>
        resp_body_raw() |>
        writeBin(con = name)
    }
    
    n_row <- if (!is.null(st_read(name, quiet = TRUE))) {
      nrow(st_read(name, quiet = TRUE))}
    else 0
    
    if (n_row < limit) {
      temp <- FALSE} 
    else {
      offset <- offset + limit
      page <- page + 1}
  }
  
  geo_file <- dir_ls(mp03, glob = "*.geojson")
  geo_data <- lapply(geo_file, st_read, quiet = TRUE) |>
    lapply(mutate, planteddate = as.character(planteddate))
  
  result <- bind_rows(geo_data)
  
  return(result)
}

tree <- Tree_Points("https://data.cityofnewyork.us/resource/hn5i-inap.geojson")

Task # 3: Plot All Tree Points

suppressPackageStartupMessages({
  library(sf)
  library(ggplot2)
  library(plotly)
  library(viridis)
})


council <- st_transform(council, 4326)
tree    <- st_transform(tree, 4326)

tree_df <- cbind(
  st_drop_geometry(tree),
  as.data.frame(st_coordinates(tree))
)
names(tree_df)[(ncol(tree_df)-1):ncol(tree_df)] <- c("lon","lat")


p <- ggplot() +
  geom_sf(data = council, fill = NA, color = "gray30", size = 0.4) +
  stat_bin_hex(
    data = tree_df,
    aes(x = lon, y = lat),
    bins = 100
  ) +
  scale_fill_gradientn(
    colors = c("#440154", "#ff5e00", "#ffaa00", "#ffff99"),
    name = "Density of Trees"
  ) +
  xlab("Longitude") +
  ylab("Latitude") +
  labs(title = "🌇 NYC Trees in the City Council Districts") +
  theme_minimal(base_size = 12) +
  theme(
    panel.background = element_rect(fill = "#f8f8f8", color = NA),
    plot.title = element_text(face = "bold", color = "#333333", size = 14, hjust = 0.5)
  ) +
  coord_sf()

ggplotly(p)

Task # 4: District-Level Analysis of Tree Coverage

1.Which Council District has the most Trees?

suppressPackageStartupMessages(library(DT))

joined_data <- st_join(council, tree, join = st_contains)

joined_data |>
  st_drop_geometry() |>
  count(CounDist) |>
  arrange(desc(n)) |>
  rename(`Council District` = CounDist,
         `Number of Trees` = n) |>
  datatable(options = list(searching = FALSE, info = FALSE)) 

District 51 ranks first with about 70,927 trees, followed by District 50 and District 19 with 52,439 and 49,832 trees respectively. Overall, the table highlights areas with particularly dense tree coverage, indicating stronger urban forestry or larger green space presence in those districts.

2.Which Council District Has The Highest Density of Trees?

joined_data |>
  st_drop_geometry() |>
  group_by(CounDist) |>
  summarize(`Number of Trees` = n(),   
            `Area Size`  = mean(Shape_Area)) |>
  mutate(Density = `Number of Trees` / `Area Size`) |>
  arrange(desc(Density)) |>
  rename(`Council District` = CounDist) |>
  datatable(options = list(searching = FALSE, info = FALSE)) 

District 7 shows the highest tree density, followed closely by Districts 39 and 2, meaning these areas have more trees relative to their land size. Overall, the table highlights districts where trees are more concentrated, reflecting better canopy coverage and greener urban environments despite possibly smaller geographic areas.

3. Which District Has Highest Fraction of Dead Trees Out of All Trees?

joined_data |>
  st_drop_geometry() |>
  group_by(CounDist) |>
  summarize(`Number of Trees` = n(),
            `Number of Dead Trees`  = sum(tpcondition == "Dead", na.rm = TRUE),
            `Dead Trees Fraction` = `Number of Dead Trees` / `Number of Trees`) |>
  arrange(desc(`Dead Trees Fraction`)) |>
  rename(`Council District` = CounDist) |>
  datatable(options = list(searching = FALSE, info = FALSE))

District 32 has the highest proportion of dead trees, with about 14.2% of all its trees classified as dead, followed closely by Districts 30 and 2. The results highlight areas where tree maintenance and replacement efforts may be most urgently needed to sustain healthy urban forest coverage.

4. What is the Most Common Tree Species in Manhattan?

joined_data <- joined_data |>
  mutate(Borough = case_when(CounDist >= 1  & CounDist <= 10 ~ "Manhattan",
                             CounDist >= 11 & CounDist <= 18 ~ "Bronx",
                             CounDist >= 19 & CounDist <= 32 ~ "Queens",
                             CounDist >= 33 & CounDist <= 48 ~ "Brooklyn",
                             CounDist >= 49 & CounDist <= 51 ~ "Staten Island"))

joined_data |>
  st_drop_geometry() |>
  filter(Borough == "Manhattan") |>
  count(genusspecies) |>
  arrange(desc(n)) |>
  rename(`Tree Species` = genusspecies,
         `Number of Trees` = n) |>
  datatable(options = list(searching = FALSE, info = FALSE)) 

The Thornless Honeylocust (Gleditsia triacanthos var. inermis) is the most abundant species, followed by the London planetree and the Callery pear. Overall, the data reveals that Manhattan’s urban forest is dominated by hardy, pollution-tolerant species well-suited to dense city environments.

5. What is the Species of the Tree Closest to Baruch’s Campus?

new_st_point <- function(lat, lon){
  st_sfc(st_point(c(lon, lat))) |>
    st_set_crs("WGS84")
}

Baruch_location <- new_st_point(lat = 40.7403, lon = -73.9833)

joined_data |>
  filter(Borough == "Manhattan") |> 
  select(geometry, genusspecies) |>
  mutate(distance = st_distance(geometry, Baruch_location)) |>
  arrange(distance) |>
  slice(1) |>
  pull(genusspecies)

[1] "Gleditsia triacanthos var. inermis - Thornless honeylocust"

The tree species closest to Baruch’s campus is the Thornless Honeylocust (Gleditsia triacanthos var. inermis), a resilient species commonly used in urban landscaping for its durability and light, filtered shade.

Task # 5: NYC Parks Proposal

GOVERNMENT PROJECT DESIGN — “District 29: Forest Hills Tree Renewal Initiative”

suppressPackageStartupMessages({
  library(sf)
  library(dplyr)
  library(ggplot2)
  library(plotly)
  library(viridis)
})

# Transform CRS
council <- st_transform(council, 4326)
tree <- st_transform(tree, 4326)

# Select District 29 (Forest Hills / Rego Park)
my_district <- 29
district_geom <- council %>% filter(CounDist == my_district)

# Filter trees within the district
tree_district <- tree[district_geom, , op = st_within]

---

Project Description and Scope

Forest Hills Tree Renewal Initiative – NYC Council District 29

Project Description:
District 29, which includes Forest Hills and Rego Park, has one of the most established tree canopies in Queens. However, recent NYC tree census data shows signs of aging tree populations, limited species diversity, and a growing number of trees in poor or dead condition—especially along major corridors like Queens Blvd, 67th Ave, and Yellowstone Blvd.
The Forest Hills Tree Renewal Initiative aims to replace aging trees, introduce native species, and engage the community through education and planting events.

Project Scope:
- Replace 400 trees in dead or poor condition (tpcondition = 'Dead' or 'Poor').
- Plant 600 new native species (e.g., Red Maple, Sweetgum, American Linden).
- Launch a “Community Canopy Day” in MacDonald Park to involve residents in tree planting and stewardship.

Estimated Budget:
Approximately $1.0–1.3 million, including removal, planting, and two years of maintenance.

Zoomed-in Map of Forest Hills / Rego Park (District 29)

p_zoom <- ggplot() +
  geom_sf(data = district_geom, fill = NA, color = "gray30", size = 0.5) +
  geom_sf(data = tree_district, aes(color = tpcondition), size = 0.6, alpha = 0.7) +
  scale_color_manual(
    values = c("Alive" = "#2ca25f", "Poor" = "#ffb703", "Dead" = "#d7191c"),
    name = "Tree Condition"
  ) +
  labs(
    title = "🌳 Tree Conditions in NYC Council District 29 (Forest Hills / Rego Park)",
    subtitle = "Based on NYC Tree Census data"
  ) +
  theme_minimal(base_size = 12) +
  coord_sf(xlim = st_bbox(district_geom)[c("xmin","xmax")],
           ylim = st_bbox(district_geom)[c("ymin","ymax")],
           expand = FALSE)

ggplotly(p_zoom)

Quantitative Comparison with Neighboring Districts

# 4) Quantitative Comparison with Neighboring Districts (FIX)

# 4.1: attach district id to each tree (point-in-polygon)
tree_with_dist <- st_join(
  tree, 
  council["CounDist"], 
  join = st_within, 
  left = FALSE  # descarta puntos fuera de cualquier distrito
)

# 4.2: counts by district (drop geometry before summarising)
tree_counts <- tree_with_dist |>
  st_drop_geometry() |>
  dplyr::group_by(CounDist) |>
  dplyr::summarise(
    n_trees = dplyr::n(),
    n_dead  = sum(tpcondition == "Dead", na.rm = TRUE),
    n_poor  = sum(tpcondition == "Poor", na.rm = TRUE),
    .groups = "drop"
  )

# 4.3: area by district (from council polygons)
district_area <- council |>
  dplyr::mutate(area_km2 = as.numeric(st_area(geometry)) / 1e6) |>
  st_drop_geometry() |>
  dplyr::select(CounDist, area_km2)

# 4.4: combine counts + area and compute rates
tree_density <- tree_counts |>
  dplyr::left_join(district_area, by = "CounDist") |>
  dplyr::mutate(
    trees_per_km2    = n_trees / area_km2,
    bad_condition_rate = 100 * (n_dead + n_poor) / n_trees
  )

# 4.5: pick comparison districts (Queens neighbors)
compare_districts <- tree_density |>
  dplyr::filter(CounDist %in% c(29, 28, 30, 32))

compare_districts

# A tibble: 4 × 7
  CounDist n_trees n_dead n_poor area_km2 trees_per_km2 bad_condition_rate
     <int>   <int>  <int>  <int>    <dbl>         <dbl>              <dbl>
1       28   25531   3108   1592     16.3         1571.               18.4
2       29   19988   2679    893     11.9         1685.               17.9
3       30   23000   3227   1508     15.7         1469.               20.6
4       32   30261   4304   2050     33.3          909.               21.0

Non-map Visualization: Comparison Bar Chart

ggplot(compare_districts, aes(x = factor(CounDist), y = bad_condition_rate, fill = factor(CounDist))) +
  geom_bar(stat = "identity", color = "black") +
  scale_fill_manual(values = c("#f94144", "#f3722c", "#f9c74f", "#90be6d")) +
  labs(
    title = "🌲 Percentage of Trees in Poor or Dead Condition by District (Queens)",
    x = "NYC Council District",
    y = "% of Trees in Poor/Dead Condition",
    fill = "District"
  ) +
  theme_minimal(base_size = 12)

Map-based Comparison Across Queens Districts

compare_map <- ggplot() +
  geom_sf(
    data = council %>% filter(CounDist %in% c(28, 29, 30, 32)),
    aes(fill = factor(CounDist)),
    color = "gray40", alpha = 0.7
  ) +
  geom_sf(
    data = tree_with_dist %>% filter(CounDist %in% c(28, 29, 30, 32)),
    color = "darkgreen", size = 0.05, alpha = 0.4
  ) +
  scale_fill_brewer(palette = "Set2", name = "District") +
  labs(title = "Tree Distribution Across Selected Queens Districts") +
  theme_minimal(base_size = 12)

compare_map

Conclusion

District 29 (Forest Hills / Rego Park) presents a compelling case for targeted tree renewal:
- It has one of the highest rates of trees in poor or dead condition among neighboring Queens districts.
- Tree density per km² is below the borough average, especially along commercial corridors.
- Strategic replanting and species diversification will improve air quality, shade equity, and community well-being.

The Forest Hills Tree Renewal Initiative aligns with the NYC Parks Department’s urban forest goals, promoting sustainability, biodiversity, and civic engagement.